{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "from torch.autograd.function import Function\n",
    "import os\n",
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "from itertools import chain\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "import itertools\n",
    "import random\n",
    "import tqdm\n",
    "from IPython.display import display, HTML\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import signal\n",
    "from scipy.fft import fftshift\n",
    "\n",
    "from einops import rearrange, repeat\n",
    "from einops.layers.torch import Rearrange\n",
    "from torch import nn, einsum\n",
    "import math\n",
    "import logging\n",
    "from functools import partial\n",
    "from collections import OrderedDict\n",
    "from sklearn.metrics import classification_report\n",
    "from torchsummary import summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wed Aug 18 22:54:55 2021       \r\n",
      "+-----------------------------------------------------------------------------+\r\n",
      "| NVIDIA-SMI 460.91.03    Driver Version: 460.91.03    CUDA Version: 11.2     |\r\n",
      "|-------------------------------+----------------------+----------------------+\r\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\r\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\r\n",
      "|                               |                      |               MIG M. |\r\n",
      "|===============================+======================+======================|\r\n",
      "|   0  GeForce RTX 208...  Off  | 00000000:01:00.0 Off |                  N/A |\r\n",
      "| 35%   25C    P8    30W / 260W |   2482MiB / 11016MiB |      0%      Default |\r\n",
      "|                               |                      |                  N/A |\r\n",
      "+-------------------------------+----------------------+----------------------+\r\n",
      "                                                                               \r\n",
      "+-----------------------------------------------------------------------------+\r\n",
      "| Processes:                                                                  |\r\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\r\n",
      "|        ID   ID                                                   Usage      |\r\n",
      "|=============================================================================|\r\n",
      "|    0   N/A  N/A     25569      C   /opt/anaconda3/bin/python         717MiB |\r\n",
      "|    0   N/A  N/A     30972      C   /opt/anaconda3/bin/python         793MiB |\r\n",
      "|    0   N/A  N/A    100395      C   /opt/anaconda3/bin/python         969MiB |\r\n",
      "+-----------------------------------------------------------------------------+\r\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!nvidia-smi\n",
    "torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(692382, 32, 30)\n",
      "(692382, 32, 30)\n"
     ]
    }
   ],
   "source": [
    "###Read the EEG Spectrogram###\n",
    "\n",
    "dat1 = np.load('/project/hikaku_db/data/sleep_SHHS/stages_sig/C4_spec_30_np/spec_1601_2308_30.npy')\n",
    "dat2 = np.load('/project/hikaku_db/data/sleep_SHHS/stages_sig/C3_spec_30_np/spec_c3_1601_2308_30.npy')\n",
    "print(dat1.shape)\n",
    "print(dat2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(692382, 2, 32, 30)\n",
      "(692382, 2, 20, 30)\n"
     ]
    }
   ],
   "source": [
    "dat = np.concatenate((dat1.reshape(-1,1,32,30), dat2.reshape(-1,1,32,30)), axis=1)\n",
    "print(dat.shape)        \n",
    "fixdata = dat[:,:,0:16,:]  \n",
    "mean_p1 = np.mean(dat[:,:,16:20,:], axis = 2)\n",
    "mean_p2 = np.mean(dat[:,:,20:24,:], axis = 2)\n",
    "mean_p3 = np.mean(dat[:,:,24:28,:], axis = 2)\n",
    "mean_p4 = np.mean(dat[:,:,28:32,:], axis = 2)\n",
    "num_data = len(dat)\n",
    "ch = 2\n",
    "inputdat = np.concatenate((fixdata,mean_p1.reshape(num_data, ch, 1, 30),mean_p2.reshape(num_data, ch, 1, 30),mean_p3.reshape(num_data, ch, 1, 30),mean_p4.reshape(num_data, ch, 1, 30)),axis=2)\n",
    "print(inputdat.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        0\n",
      "2  283199\n",
      "0  208397\n",
      "4   88394\n",
      "3   87602\n",
      "1   24790\n",
      "          0\n",
      "0  0.118956\n",
      "1  1.000000\n",
      "2  0.087536\n",
      "3  0.282984\n",
      "4  0.280449\n"
     ]
    }
   ],
   "source": [
    "###Read the Lable###\n",
    "index = pd.read_csv(\"/project/hikaku_db/data/sleep_SHHS/stages_sig/ann_delrecords_5class.csv\", header=None)\n",
    "index = index[1594504 : 2286886].astype(int)\n",
    "print(index.apply(pd.value_counts))\n",
    "label = index.values.tolist()  #list\n",
    "weight = 1/(index.apply(pd.value_counts)/len(index))\n",
    "weight = (weight/weight.max()).sort_index()\n",
    "print(weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Mydatasets(torch.utils.data.Dataset):\n",
    "    def __init__(self, data,label ,transform = None):\n",
    "        self.transform = transform\n",
    "\n",
    "        self.data = data\n",
    "        self.label = label\n",
    "\n",
    "        self.datanum = len(data)\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.datanum\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        \n",
    "        out_data = torch.tensor(self.data[idx]).float()\n",
    "        out_label = torch.tensor(self.label[idx])\n",
    "        if self.transform:\n",
    "            out_data = self.transform(out_data)\n",
    "\n",
    "        return out_data, out_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train data: 623143\n",
      "test data: 69239\n"
     ]
    }
   ],
   "source": [
    "train, test, train_label, test_label = train_test_split(inputdat, np.array(label),test_size = 0.1,stratify = label, random_state = 0)\n",
    "print('train data:',len(train))\n",
    "print('test data:',len(test))\n",
    "\n",
    "train_data_set = Mydatasets(data=train,label=train_label)\n",
    "test_data_set = Mydatasets(data=test,label=test_label)\n",
    "\n",
    "train_dataloader = torch.utils.data.DataLoader(train_data_set, batch_size = 64, shuffle=True)\n",
    "test_dataloader = torch.utils.data.DataLoader(test_data_set, batch_size = 64, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LSTM(nn.Module):\n",
    "    def __init__(self,hidden_size,num_layers):\n",
    "        super().__init__()\n",
    "        self.to_patch = nn.Sequential(\n",
    "            #(batch, 4, 20, 30) -> (batch, 150, 16)  or  (batch, 30, 80)\n",
    "            Rearrange('b c (h p1) (w p2) -> b (h w p2) (c p1)', p1 = 4, p2 = 30),  \n",
    "        )\n",
    "        \n",
    "        self.lstm = nn.LSTM(\n",
    "            input_size = 8,\n",
    "            hidden_size = hidden_size,\n",
    "            num_layers = num_layers,\n",
    "            batch_first=True\n",
    "        )\n",
    "        \n",
    "    def forward(self,x):\n",
    "        # output: [batch_size, time_step, hidden_size] ,same as input: [batch, seq_len, input_size](guide name)\n",
    "        # LSTM : batch_first = True, but the first dim of h_n and c_n will still be num_layers\n",
    "        # h_n and c_n: [num_layers,batch_size, hidden_size] \n",
    "        x = self.to_patch(x)\n",
    "        mean_out = True            #use mean_out of LSTM as output \n",
    "        output,(h_n,c_n)=self.lstm(x) \n",
    "        out = output[:,-1,:]     #output of the last timestep\n",
    "        if mean_out:\n",
    "            out = output.mean(dim = 1)      #mean of all timestep output\n",
    "        return out\n",
    "\n",
    "##LSTM model\n",
    "\n",
    "class LSTMModel(nn.Module):\n",
    "    def __init__(self,hidden_size,num_layers):\n",
    "        super().__init__()     \n",
    "        self.lstm = LSTM(hidden_size,num_layers)\n",
    "    \n",
    "        self.to_latent = nn.Identity()\n",
    "        \n",
    "        self.mlp_head = nn.Sequential(\n",
    "            nn.LayerNorm(hidden_size),\n",
    "            nn.Linear(hidden_size, 5)\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.lstm(x)        \n",
    "        x = self.to_latent(x)\n",
    "        return self.mlp_head(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(DEVICE)\n",
    "\n",
    "LSTMModel = LSTMModel(\n",
    "    hidden_size = 32,\n",
    "    num_layers = 8\n",
    ").to(DEVICE)\n",
    "\n",
    "#summary(Transmodel,(2286886, 20, 30))\n",
    "\n",
    "criterion = torch.nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.AdamW(LSTMModel.parameters(), lr=1e-4)\n",
    "\n",
    "LSTMModel.load_state_dict(torch.load('/project/hikaku_db/ziwei/Model_11/Model_11_state_2'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pytorch_total_params = sum(p.numel() for p in LSTMModel.parameters())\n",
    "# pytorch_total_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "\r",
      "  0%|          | 0/100 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1 :finished\n",
      "train_loss: 0.4704728583048976\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:40: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  1%|          | 1/100 [06:25<10:35:56, 385.42s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_loss: 0.474704639721049\n",
      "{'0': {'precision': 0.8706352732119058, 'recall': 0.9404030710172745, 'f1-score': 0.9041753171856979, 'support': 20840}, '1': {'precision': 0.3989071038251366, 'recall': 0.02944735780556676, 'f1-score': 0.05484598046581518, 'support': 2479}, '2': {'precision': 0.8360782930700053, 'recall': 0.837111581920904, 'f1-score': 0.8365946184384648, 'support': 28320}, '3': {'precision': 0.8636541726524749, 'recall': 0.7549086757990867, 'f1-score': 0.8056283121154899, 'support': 8760}, '4': {'precision': 0.665938864628821, 'recall': 0.793552036199095, 'f1-score': 0.7241664085888304, 'support': 8840}, 'accuracy': 0.8233221161484134, 'macro avg': {'precision': 0.7270427414776688, 'recall': 0.6710845445483853, 'f1-score': 0.6650821273588596, 'support': 69239}, 'weighted avg': {'precision': 0.8125937286733727, 'recall': 0.8233221161484134, 'f1-score': 0.8106742075413997, 'support': 69239}}\n",
      "epoch 2 :finished\n",
      "train_loss: 0.45438439103261\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_loss: 0.45750215633078556\n",
      "{'0': {'precision': 0.9221151975092431, 'recall': 0.9095489443378119, 'f1-score': 0.9157889651174026, 'support': 20840}, '1': {'precision': 0.4012345679012346, 'recall': 0.07866075030254134, 'f1-score': 0.1315345699831366, 'support': 2479}, '2': {'precision': 0.8260248827248623, 'recall': 0.8580508474576272, 'f1-score': 0.8417333471890263, 'support': 28320}, '3': {'precision': 0.7986141186660892, 'recall': 0.8420091324200913, 'f1-score': 0.8197377194932207, 'support': 8760}, '4': {'precision': 0.7013517761710154, 'recall': 0.7571266968325792, 'f1-score': 0.7281727683185553, 'support': 8840}, 'accuracy': 0.8307312352864715, 'macro avg': {'precision': 0.7298681085944889, 'recall': 0.6890792742701302, 'f1-score': 0.6873934740202683, 'support': 69239}, 'weighted avg': {'precision': 0.8203523342271639, 'recall': 0.8307312352864715, 'f1-score': 0.8213139172883018, 'support': 69239}}\n",
      "epoch 3 :finished\n",
      "train_loss: 0.44801533587037434\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_loss: 0.4465283820347072\n",
      "{'0': {'precision': 0.9003915353780181, 'recall': 0.9269193857965451, 'f1-score': 0.9134629025393673, 'support': 20840}, '1': {'precision': 0.36855036855036855, 'recall': 0.12101653892698669, 'f1-score': 0.18220467658669906, 'support': 2479}, '2': {'precision': 0.8349191083021331, 'recall': 0.85829802259887, 'f1-score': 0.8464471645221389, 'support': 28320}, '3': {'precision': 0.8338576255388559, 'recall': 0.8170091324200913, 'f1-score': 0.8253474024101943, 'support': 8760}, '4': {'precision': 0.7215094339622642, 'recall': 0.7570135746606335, 'f1-score': 0.7388352194314105, 'support': 8840}, 'accuracy': 0.834399688037089, 'macro avg': {'precision': 0.731845614346328, 'recall': 0.6960513308806253, 'f1-score': 0.701259473097962, 'support': 69239}, 'weighted avg': {'precision': 0.8233140470540802, 'recall': 0.834399688037089, 'f1-score': 0.8264270507421074, 'support': 69239}}\n",
      "epoch 4 :finished\n",
      "train_loss: 0.4435063808838102\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "val_loss: 0.44524843154850374\n",
      "{'0': {'precision': 0.9090437570728027, 'recall': 0.9250959692898273, 'f1-score': 0.9169996194824962, 'support': 20840}, '1': {'precision': 0.3976311336717428, 'recall': 0.09479628882613957, 'f1-score': 0.1530944625407166, 'support': 2479}, '2': {'precision': 0.834841472680804, 'recall': 0.8535310734463277, 'f1-score': 0.8440828299053672, 'support': 28320}, '3': {'precision': 0.8190881595160748, 'recall': 0.8347031963470319, 'f1-score': 0.8268219596313676, 'support': 8760}, '4': {'precision': 0.7131499110785647, 'recall': 0.7711538461538462, 'f1-score': 0.7410185336159574, 'support': 8840}, 'accuracy': 0.8350062825864036, 'macro avg': {'precision': 0.7347508868039978, 'recall': 0.6958560748126346, 'f1-score': 0.696403481035181, 'support': 69239}, 'weighted avg': {'precision': 0.8239917889539876, 'recall': 0.8350062825864036, 'f1-score': 0.825947561188196, 'support': 69239}}\n",
      "epoch 5 :finished\n",
      "train_loss: 0.44027356239630844\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "val_loss: 0.44339997828172006\n",
      "{'0': {'precision': 0.9195153613154479, 'recall': 0.9177063339731286, 'f1-score': 0.9186099570114556, 'support': 20840}, '1': {'precision': 0.3776435045317221, 'recall': 0.050423557886244454, 'f1-score': 0.0889679715302491, 'support': 2479}, '2': {'precision': 0.8340230514039468, 'recall': 0.8610875706214689, 'f1-score': 0.8473392518980525, 'support': 28320}, '3': {'precision': 0.8355332320990538, 'recall': 0.8165525114155251, 'f1-score': 0.8259338375382483, 'support': 8760}, '4': {'precision': 0.6860995246871665, 'recall': 0.8001131221719457, 'f1-score': 0.7387330931119119, 'support': 8840}, 'accuracy': 0.8356850907725415, 'macro avg': {'precision': 0.7305629348074674, 'recall': 0.6891766192136626, 'f1-score': 0.6839168222179834, 'support': 69239}, 'weighted avg': {'precision': 0.8247202025553535, 'recall': 0.8356850907725415, 'f1-score': 0.8250640777486594, 'support': 69239}}\n",
      "epoch 6 :finished\n",
      "train_loss: 0.4370835672628222\n"
     ]
    },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_loss: 0.4460162532252437\n",
      "{'0': {'precision': 0.9282743774157248, 'recall': 0.9104126679462572, 'f1-score': 0.9192567649410113, 'support': 20840}, '1': {'precision': 0.3732590529247911, 'recall': 0.05405405405405406, 'f1-score': 0.09443269908386188, 'support': 2479}, '2': {'precision': 0.8242007608623106, 'recall': 0.8721045197740113, 'f1-score': 0.8474762378615791, 'support': 28320}, '3': {'precision': 0.8310607811416686, 'recall': 0.8210045662100457, 'f1-score': 0.8260020673021706, 'support': 8760}, '4': {'precision': 0.6991141431626108, 'recall': 0.7766968325791855, 'f1-score': 0.7358662451101228, 'support': 8840}, 'accuracy': 0.8356995334999061, 'macro avg': {'precision': 0.7311818231014212, 'recall': 0.6868545281127107, 'f1-score': 0.6846068028597492, 'support': 69239}, 'weighted avg': {'precision': 0.8242778525617557, 'recall': 0.8356995334999061, 'f1-score': 0.8251530555753259, 'support': 69239}}\n",
      "epoch 7 :finished\n",
      "train_loss: 0.43493130933189217\n"
     ]
    },
    {
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     "text": [
      "val_loss: 0.45360592002128275\n",
      "{'0': {'precision': 0.89950004629201, 'recall': 0.9323896353166986, 'f1-score': 0.9156495923849018, 'support': 20840}, '1': {'precision': 0.3527397260273973, 'recall': 0.08309802339653086, 'f1-score': 0.1345086516487104, 'support': 2479}, '2': {'precision': 0.7932371645726695, 'recall': 0.9028954802259888, 'f1-score': 0.8445215093716456, 'support': 28320}, '3': {'precision': 0.855164808234285, 'recall': 0.796689497716895, 'f1-score': 0.824892145854264, 'support': 8760}, '4': {'precision': 0.8116268589454709, 'recall': 0.6111990950226245, 'f1-score': 0.6972962508872685, 'support': 8840}, 'accuracy': 0.831742226201996, 'macro avg': {'precision': 0.7424537208143664, 'recall': 0.6652543463357475, 'f1-score': 0.683373630029358, 'support': 69239}, 'weighted avg': {'precision': 0.8196323517014357, 'recall': 0.831742226201996, 'f1-score': 0.8192288688986022, 'support': 69239}}\n",
      "epoch 8 :finished\n",
      "train_loss: 0.4326955848711588\n"
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      "val_loss: 0.445550104744333\n",
      "{'0': {'precision': 0.8966674327740749, 'recall': 0.9360364683301343, 'f1-score': 0.9159290996595845, 'support': 20840}, '1': {'precision': 0.38317757009345793, 'recall': 0.06615570794675273, 'f1-score': 0.11283109735122118, 'support': 2479}, '2': {'precision': 0.8431255905098506, 'recall': 0.8507768361581921, 'f1-score': 0.8469339332477986, 'support': 28320}, '3': {'precision': 0.858636137381782, 'recall': 0.7876712328767124, 'f1-score': 0.8216241962371995, 'support': 8760}, '4': {'precision': 0.6852437039164991, 'recall': 0.8095022624434389, 'f1-score': 0.7422081626302962, 'support': 8840}, 'accuracy': 0.8350929389505914, 'macro avg': {'precision': 0.733370086935133, 'recall': 0.6900285015510461, 'f1-score': 0.68790529782522, 'support': 69239}, 'weighted avg': {'precision': 0.8245782019468598, 'recall': 0.8350929389505914, 'f1-score': 0.8248442038953012, 'support': 69239}}\n",
      "epoch 9 :finished\n",
      "train_loss: 0.4308086430051922\n"
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      "val_loss: 0.4408242210690724\n",
      "{'0': {'precision': 0.9235567159849144, 'recall': 0.9165547024952015, 'f1-score': 0.9200423871682482, 'support': 20840}, '1': {'precision': 0.41509433962264153, 'recall': 0.07987091569181122, 'f1-score': 0.13396481732070367, 'support': 2479}, '2': {'precision': 0.8121863338051731, 'recall': 0.891454802259887, 'f1-score': 0.8499764325634638, 'support': 28320}, '3': {'precision': 0.8655365791486107, 'recall': 0.7752283105022831, 'f1-score': 0.8178971456100206, 'support': 8760}, '4': {'precision': 0.7256830601092896, 'recall': 0.751131221719457, 'f1-score': 0.7381878821567538, 'support': 8840}, 'accuracy': 0.83733156169211, 'macro avg': {'precision': 0.7484114057341258, 'recall': 0.682847990533728, 'f1-score': 0.6920137329638381, 'support': 69239}, 'weighted avg': {'precision': 0.8271956048920492, 'recall': 0.83733156169211, 'f1-score': 0.8270985221440386, 'support': 69239}}\n",
      "epoch 10 :finished\n",
      "train_loss: 0.4290110618962612\n"
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      "val_loss: 0.4378064507945628\n",
      "{'0': {'precision': 0.9205107281716507, 'recall': 0.9202015355086373, 'f1-score': 0.9203561058719075, 'support': 20840}, '1': {'precision': 0.410958904109589, 'recall': 0.060508269463493344, 'f1-score': 0.10548523206751054, 'support': 2479}, '2': {'precision': 0.8349338710228632, 'recall': 0.8626765536723164, 'f1-score': 0.8485785241659576, 'support': 28320}, '3': {'precision': 0.8249830737982397, 'recall': 0.8345890410958904, 'f1-score': 0.8297582567245488, 'support': 8760}, '4': {'precision': 0.7047791893526921, 'recall': 0.7907239819004525, 'f1-score': 0.7452820130077833, 'support': 8840}, 'accuracy': 0.8385303080633747, 'macro avg': {'precision': 0.7392331532910068, 'recall': 0.6937398763281579, 'f1-score': 0.6898920263675415, 'support': 69239}, 'weighted avg': {'precision': 0.8276352588296009, 'recall': 0.8385303080633747, 'f1-score': 0.8280078895556215, 'support': 69239}}\n",
      "epoch 11 :finished\n",
      "train_loss: 0.4274002901266289\n"
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      "{'0': {'precision': 0.9048592399888506, 'recall': 0.9346449136276391, 'f1-score': 0.9195109285748004, 'support': 20840}, '1': {'precision': 0.411522633744856, 'recall': 0.08067769261799113, 'f1-score': 0.13490725126475547, 'support': 2479}, '2': {'precision': 0.8365935114503816, 'recall': 0.8668432203389831, 'f1-score': 0.8514497780244175, 'support': 28320}, '3': {'precision': 0.8426078755010611, 'recall': 0.8158675799086758, 'f1-score': 0.8290221552024127, 'support': 8760}, '4': {'precision': 0.7306669503244335, 'recall': 0.7770361990950226, 'f1-score': 0.7531385340715969, 'support': 8840}, 'accuracy': 0.8411877698984677, 'macro avg': {'precision': 0.7452500422019166, 'recall': 0.6950139211176622, 'f1-score': 0.6976057294275966, 'support': 69239}, 'weighted avg': {'precision': 0.8291584258142556, 'recall': 0.8411877698984677, 'f1-score': 0.8308912500440753, 'support': 69239}}\n",
      "epoch 12 :finished\n",
      "train_loss: 0.42589265005880156\n"
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      "val_loss: 0.43585885769227956\n",
      "{'0': {'precision': 0.9123684583077721, 'recall': 0.9277351247600768, 'f1-score': 0.9199876281792011, 'support': 20840}, '1': {'precision': 0.3716129032258065, 'recall': 0.11617587736990723, 'f1-score': 0.17701290719114937, 'support': 2479}, '2': {'precision': 0.8251411768637017, 'recall': 0.8719632768361582, 'f1-score': 0.8479063299397394, 'support': 28320}, '3': {'precision': 0.8153964419064353, 'recall': 0.8476027397260274, 'f1-score': 0.8311877308854808, 'support': 8760}, '4': {'precision': 0.762378640776699, 'recall': 0.710633484162896, 'f1-score': 0.7355971896955504, 'support': 8840}, 'accuracy': 0.8380103698782478, 'macro avg': {'precision': 0.737379524216083, 'recall': 0.6948221005710131, 'f1-score': 0.7023383571782242, 'support': 69239}, 'weighted avg': {'precision': 0.825911483449749, 'recall': 0.8380103698782478, 'f1-score': 0.8291273431381202, 'support': 69239}}\n",
      "epoch 13 :finished\n",
      "train_loss: 0.4244465627708976\n"
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      "val_loss: 0.43510242132900384\n",
      "{'0': {'precision': 0.9073321183232861, 'recall': 0.9316698656429943, 'f1-score': 0.9193399464949454, 'support': 20840}, '1': {'precision': 0.3969849246231156, 'recall': 0.09560306575231949, 'f1-score': 0.15409622886866062, 'support': 2479}, '2': {'precision': 0.8194139793746931, 'recall': 0.8837923728813559, 'f1-score': 0.850386477533339, 'support': 28320}, '3': {'precision': 0.841956393635828, 'recall': 0.8155251141552512, 'f1-score': 0.8285300086981734, 'support': 8760}, '4': {'precision': 0.7656154876415439, 'recall': 0.7113122171945702, 'f1-score': 0.737465548583827, 'support': 8840}, 'accuracy': 0.8393246580684296, 'macro avg': {'precision': 0.7462605807196934, 'recall': 0.6875805271252983, 'f1-score': 0.6979636420357891, 'support': 69239}, 'weighted avg': {'precision': 0.826735074002952, 'recall': 0.8393246580684296, 'f1-score': 0.8290286169029198, 'support': 69239}}\n",
      "epoch 14 :finished\n",
      "train_loss: 0.4235102451776863\n"
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      "{'0': {'precision': 0.9133928993523377, 'recall': 0.9271113243761996, 'f1-score': 0.9202009858785988, 'support': 20840}, '1': {'precision': 0.37681159420289856, 'recall': 0.11536910044372731, 'f1-score': 0.1766522544780729, 'support': 2479}, '2': {'precision': 0.8346075085324232, 'recall': 0.8634887005649717, 'f1-score': 0.8488024991322456, 'support': 28320}, '3': {'precision': 0.8279777449755876, 'recall': 0.8324200913242009, 'f1-score': 0.8301929754653612, 'support': 8760}, '4': {'precision': 0.733297180043384, 'recall': 0.7648190045248868, 'f1-score': 0.7487264673311184, 'support': 8840}, 'accuracy': 0.8393246580684296, 'macro avg': {'precision': 0.7372173854213262, 'recall': 0.7006416442467973, 'f1-score': 0.7049150364570794, 'support': 69239}, 'weighted avg': {'precision': 0.8281566851592251, 'recall': 0.8393246580684296, 'f1-score': 0.8310956064684641, 'support': 69239}}\n",
      "epoch 15 :finished\n",
      "train_loss: 0.422137247119778\n"
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      "val_loss: 0.4361045323381142\n",
      "{'0': {'precision': 0.919461479494059, 'recall': 0.920873320537428, 'f1-score': 0.9201668584579977, 'support': 20840}, '1': {'precision': 0.41058394160583944, 'recall': 0.09076240419524001, 'f1-score': 0.14866204162537167, 'support': 2479}, '2': {'precision': 0.8098438993309971, 'recall': 0.8976341807909605, 'f1-score': 0.8514821637916596, 'support': 28320}, '3': {'precision': 0.8833132610731969, 'recall': 0.7535388127853881, 'f1-score': 0.8132815868909012, 'support': 8760}, '4': {'precision': 0.7420723537293434, 'recall': 0.7518099547511312, 'f1-score': 0.7469094178467071, 'support': 8840}, 'accuracy': 0.8388913762474905, 'macro avg': {'precision': 0.7530549870466872, 'recall': 0.6829237346120296, 'f1-score': 0.6961004137225275, 'support': 69239}, 'weighted avg': {'precision': 0.8291849655096044, 'recall': 0.8388913762474905, 'f1-score': 0.8288076281425635, 'support': 69239}}\n",
      "epoch 16 :finished\n",
      "train_loss: 0.42101236457506147\n"
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      "val_loss: 0.4357585872277314\n",
      "{'0': {'precision': 0.8862113923481545, 'recall': 0.945873320537428, 'f1-score': 0.915070909639533, 'support': 20840}, '1': {'precision': 0.3876582278481013, 'recall': 0.09883017345703912, 'f1-score': 0.15750562520090003, 'support': 2479}, '2': {'precision': 0.8245642934800514, 'recall': 0.8837570621468926, 'f1-score': 0.8531351729074702, 'support': 28320}, '3': {'precision': 0.856760374832664, 'recall': 0.8036529680365296, 'f1-score': 0.8293573658479119, 'support': 8760}, '4': {'precision': 0.7863741339491916, 'recall': 0.6933257918552036, 'f1-score': 0.7369243717686665, 'support': 8840}, 'accuracy': 0.839902367163015, 'macro avg': {'precision': 0.7483136844916325, 'recall': 0.6850878632066186, 'f1-score': 0.6983986890728964, 'support': 69239}, 'weighted avg': {'precision': 0.8266739725064058, 'recall': 0.839902367163015, 'f1-score': 0.8290256108517334, 'support': 69239}}\n",
      "epoch 17 :finished\n",
      "train_loss: 0.41997296341307616\n"
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      "val_loss: 0.4345041076874777\n",
      "{'0': {'precision': 0.9249371251692784, 'recall': 0.917658349328215, 'f1-score': 0.9212833606320454, 'support': 20840}, '1': {'precision': 0.3929121725731895, 'recall': 0.10286405808793868, 'f1-score': 0.16304347826086954, 'support': 2479}, '2': {'precision': 0.8239121861443407, 'recall': 0.8852401129943502, 'f1-score': 0.8534758630081023, 'support': 28320}, '3': {'precision': 0.8851126216180195, 'recall': 0.7581050228310502, 'f1-score': 0.816700485765234, 'support': 8760}, '4': {'precision': 0.7113092256836622, 'recall': 0.8032805429864254, 'f1-score': 0.7545024703819794, 'support': 8840}, 'accuracy': 0.8404367480755066, 'macro avg': {'precision': 0.7476366662376981, 'recall': 0.6934296172455958, 'f1-score': 0.7018011316096461, 'support': 69239}, 'weighted avg': {'precision': 0.832254541463075, 'recall': 0.8404367480755066, 'f1-score': 0.8318760316014114, 'support': 69239}}\n",
      "epoch 18 :finished\n",
      "train_loss: 0.4191586039673441\n"
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      "val_loss: 0.43232996994543427\n",
      "{'0': {'precision': 0.9249153362360909, 'recall': 0.9173704414587333, 'f1-score': 0.921127439171284, 'support': 20840}, '1': {'precision': 0.41566265060240964, 'recall': 0.08350141185962082, 'f1-score': 0.1390661740006718, 'support': 2479}, '2': {'precision': 0.8191738875496775, 'recall': 0.8879590395480226, 'f1-score': 0.8521806906367548, 'support': 28320}, '3': {'precision': 0.8464102869389213, 'recall': 0.8115296803652968, 'f1-score': 0.8286030654467044, 'support': 8760}, '4': {'precision': 0.7400267439268999, 'recall': 0.7512443438914027, 'f1-score': 0.7455933535421578, 'support': 8840}, 'accuracy': 0.8408844726238103, 'macro avg': {'precision': 0.7492377810507997, 'recall': 0.6903209834246152, 'f1-score': 0.6973141445595146, 'support': 69239}, 'weighted avg': {'precision': 0.8298943997358298, 'recall': 0.8408844726238103, 'f1-score': 0.8308093146223362, 'support': 69239}}\n",
      "epoch 19 :finished\n",
      "train_loss: 0.4181567391135235\n"
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      "val_loss: 0.4320748440384204\n",
      "{'0': {'precision': 0.9079353210580428, 'recall': 0.9322456813819577, 'f1-score': 0.919929920924286, 'support': 20840}, '1': {'precision': 0.4177215189873418, 'recall': 0.06655909640984267, 'f1-score': 0.11482254697286011, 'support': 2479}, '2': {'precision': 0.8386853559066452, 'recall': 0.8641242937853107, 'f1-score': 0.8512148037357171, 'support': 28320}, '3': {'precision': 0.84736653529201, 'recall': 0.809931506849315, 'f1-score': 0.8282262300822973, 'support': 8760}, '4': {'precision': 0.7104305639781686, 'recall': 0.7951357466063348, 'f1-score': 0.7504003416248533, 'support': 8840}, 'accuracy': 0.8404078626207773, 'macro avg': {'precision': 0.7444278590444416, 'recall': 0.6935992650065522, 'f1-score': 0.6929187686680028, 'support': 69239}, 'weighted avg': {'precision': 0.8291802315229919, 'recall': 0.8404078626207773, 'f1-score': 0.8297518549269629, 'support': 69239}}\n",
      "epoch 20 :finished\n",
      "train_loss: 0.4174114859047839\n"
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      "val_loss: 0.43805935758539577\n",
      "{'0': {'precision': 0.925142470781416, 'recall': 0.919193857965451, 'f1-score': 0.9221585712222596, 'support': 20840}, '1': {'precision': 0.46396396396396394, 'recall': 0.08309802339653086, 'f1-score': 0.14095107765993842, 'support': 2479}, '2': {'precision': 0.8465000175790177, 'recall': 0.8501765536723164, 'f1-score': 0.8483343022743689, 'support': 28320}, '3': {'precision': 0.8563225412339646, 'recall': 0.8001141552511416, 'f1-score': 0.8272646798465624, 'support': 8760}, '4': {'precision': 0.6566617223627955, 'recall': 0.8513574660633484, 'f1-score': 0.7414413083099355, 'support': 8840}, 'accuracy': 0.8373026762373806, 'macro avg': {'precision': 0.7497181431842315, 'recall': 0.7007880112697576, 'f1-score': 0.696029987862613, 'support': 69239}, 'weighted avg': {'precision': 0.8334795612658443, 'recall': 0.8373026762373806, 'f1-score': 0.8289144780704042, 'support': 69239}}\n",
      "epoch 21 :finished\n",
      "train_loss: 0.4161913358314003\n"
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      "val_loss: 0.4303986674894246\n",
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      "epoch 22 :finished\n",
      "train_loss: 0.41530740709780817\n"
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      "val_loss: 0.42778101199876356\n",
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      "epoch 23 :finished\n",
      "train_loss: 0.41480620237846105\n"
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      "val_loss: 0.43534823905077524\n",
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      "epoch 24 :finished\n",
      "train_loss: 0.4140074477326616\n"
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      "val_loss: 0.437943673811426\n",
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      "epoch 25 :finished\n",
      "train_loss: 0.41335812984942116\n"
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      "epoch 26 :finished\n",
      "train_loss: 0.4125749691838759\n"
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      "val_loss: 0.431163097898753\n",
      "{'0': {'precision': 0.8932373057933596, 'recall': 0.946257197696737, 'f1-score': 0.9189831535289047, 'support': 20840}, '1': {'precision': 0.38501742160278746, 'recall': 0.0891488503428802, 'f1-score': 0.14477563052735015, 'support': 2479}, '2': {'precision': 0.8344540938735512, 'recall': 0.8719632768361582, 'f1-score': 0.8527964360333604, 'support': 28320}, '3': {'precision': 0.8561844352281044, 'recall': 0.8012557077625571, 'f1-score': 0.8278098832409483, 'support': 8760}, '4': {'precision': 0.7530976469250881, 'recall': 0.7494343891402715, 'f1-score': 0.7512615524182117, 'support': 8840}, 'accuracy': 0.8417077080835945, 'macro avg': {'precision': 0.7443981806845781, 'recall': 0.6916118843557209, 'f1-score': 0.699125331149755, 'support': 69239}, 'weighted avg': {'precision': 0.8284178343246127, 'recall': 0.8417077080835945, 'f1-score': 0.8312435112675247, 'support': 69239}}\n",
      "epoch 27 :finished\n",
      "train_loss: 0.4120154889399748\n"
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      "val_loss: 0.4390685386446179\n",
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      "epoch 28 :finished\n",
      "train_loss: 0.41116810734265635\n"
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      "val_loss: 0.4311769876815034\n",
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      "epoch 29 :finished\n",
      "train_loss: 0.4108692357014175\n"
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      "val_loss: 0.4251544364554164\n",
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      "epoch 30 :finished\n",
      "train_loss: 0.41009441603009944\n"
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      "val_loss: 0.4290312299308627\n",
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      "epoch 31 :finished\n",
      "train_loss: 0.40915421249808454\n"
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      "val_loss: 0.43350598059057527\n",
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      "epoch 32 :finished\n",
      "train_loss: 0.4086170829078849\n"
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      "val_loss: 0.43172610882332274\n",
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      "epoch 33 :finished\n",
      "train_loss: 0.4082364243786842\n"
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      "val_loss: 0.42388918045598345\n",
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      "epoch 34 :finished\n",
      "train_loss: 0.4074160548157916\n"
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      "val_loss: 0.4259340693819545\n",
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      "epoch 35 :finished\n",
      "train_loss: 0.4068283587663561\n"
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      "val_loss: 0.42520239128423043\n",
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      "epoch 36 :finished\n",
      "train_loss: 0.4063694413785414\n"
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      "epoch 37 :finished\n",
      "train_loss: 0.4060662020389226\n"
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      "epoch 38 :finished\n",
      "train_loss: 0.40543754213113026\n"
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      "epoch 39 :finished\n",
      "train_loss: 0.40489402041183525\n"
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      "val_loss: 0.4347806034984959\n",
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      "epoch 40 :finished\n",
      "train_loss: 0.40457958721264903\n"
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      "val_loss: 0.4299629598360185\n",
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      "epoch 41 :finished\n",
      "train_loss: 0.40414122408440534\n"
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      "epoch 42 :finished\n",
      "train_loss: 0.4032979330830938\n"
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      "epoch 43 :finished\n",
      "train_loss: 0.4032608816878218\n"
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      "epoch 44 :finished\n",
      "train_loss: 0.40274462784090803\n"
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      "val_loss: 0.4234408935507873\n",
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      "epoch 45 :finished\n",
      "train_loss: 0.40223101696292757\n"
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      "val_loss: 0.427088408794619\n",
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      "epoch 46 :finished\n",
      "train_loss: 0.4019062387125441\n"
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      "val_loss: 0.42393929351555\n",
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      "epoch 47 :finished\n",
      "train_loss: 0.40115331558062456\n"
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      "val_loss: 0.42453085347431646\n",
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      "epoch 48 :finished\n",
      "train_loss: 0.4010465387972312\n"
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      "val_loss: 0.43157904097972205\n",
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      "epoch 49 :finished\n",
      "train_loss: 0.40068794499068483\n"
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      "val_loss: 0.4292707193828775\n",
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      "epoch 50 :finished\n",
      "train_loss: 0.40032882688807503\n"
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      "epoch 51 :finished\n",
      "train_loss: 0.3995838446732577\n"
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      "epoch 52 :finished\n",
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      "epoch 53 :finished\n",
      "train_loss: 0.3984951943501905\n"
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      "epoch 54 :finished\n",
      "train_loss: 0.3985966805441737\n"
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      "val_loss: 0.4264999618426709\n",
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      "epoch 55 :finished\n",
      "train_loss: 0.3983180848427275\n"
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      "epoch 56 :finished\n",
      "train_loss: 0.3978567798671211\n"
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      "epoch 57 :finished\n",
      "train_loss: 0.3976489908980236\n"
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      "val_loss: 0.4231392882089518\n",
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      "epoch 58 :finished\n",
      "train_loss: 0.3972942987915071\n"
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      "epoch 59 :finished\n",
      "train_loss: 0.3970148438354213\n"
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      "val_loss: 0.421173706333981\n",
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      "epoch 60 :finished\n",
      "train_loss: 0.396963365188526\n"
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      "val_loss: 0.42344450736470013\n",
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      "epoch 61 :finished\n",
      "train_loss: 0.39622370879211455\n"
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      "val_loss: 0.4242583146928198\n",
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      "epoch 62 :finished\n",
      "train_loss: 0.39598547338006973\n"
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      "val_loss: 0.4438632554245524\n",
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      "epoch 63 :finished\n",
      "train_loss: 0.39587395931368824\n"
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      "epoch 64 :finished\n",
      "train_loss: 0.3952659468937547\n"
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      "epoch 65 :finished\n",
      "train_loss: 0.39528670576155717\n"
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      "epoch 66 :finished\n",
      "train_loss: 0.3946956444026276\n"
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      "epoch 67 :finished\n",
      "train_loss: 0.39453888763554523\n"
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      "val_loss: 0.4268279456965584\n",
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      "epoch 68 :finished\n",
      "train_loss: 0.3940189138071682\n"
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      "val_loss: 0.42295968722614036\n",
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      "epoch 69 :finished\n",
      "train_loss: 0.3937698552373304\n"
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      "epoch 70 :finished\n",
      "train_loss: 0.3932071830444149\n"
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      "epoch 71 :finished\n",
      "train_loss: 0.393588833646313\n"
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      "epoch 72 :finished\n",
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      "epoch 73 :finished\n",
      "train_loss: 0.39266744564388084\n"
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      "val_loss: 0.4262014137729038\n",
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      "epoch 74 :finished\n",
      "train_loss: 0.3921630394497642\n"
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      "val_loss: 0.42748822008781207\n",
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      "epoch 75 :finished\n",
      "train_loss: 0.3922725917087194\n"
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      "val_loss: 0.4237782258106578\n",
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      "epoch 76 :finished\n",
      "train_loss: 0.39185009786721997\n"
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      "val_loss: 0.4234088858221133\n",
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      "epoch 77 :finished\n",
      "train_loss: 0.3919396962263849\n"
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      "epoch 78 :finished\n",
      "train_loss: 0.3914933002742211\n"
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      "epoch 79 :finished\n",
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      "epoch 80 :finished\n",
      "train_loss: 0.3911414340863828\n"
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      "epoch 81 :finished\n",
      "train_loss: 0.3905441298816096\n"
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      "epoch 82 :finished\n",
      "train_loss: 0.39049021538457573\n"
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      "val_loss: 0.42824143149275434\n",
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      "epoch 83 :finished\n",
      "train_loss: 0.39015009907453324\n"
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      "epoch 84 :finished\n",
      "train_loss: 0.39025037022015024\n"
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      "epoch 85 :finished\n",
      "train_loss: 0.3898094808056306\n"
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      "epoch 86 :finished\n",
      "train_loss: 0.3895350472661667\n"
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      "epoch 87 :finished\n",
      "train_loss: 0.38952840350455814\n"
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      "val_loss: 0.4257167456413373\n",
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      "epoch 88 :finished\n",
      "train_loss: 0.38903273545650163\n"
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      "val_loss: 0.42102654582636634\n",
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      "epoch 89 :finished\n",
      "train_loss: 0.3890305697746537\n"
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      "val_loss: 0.42722893598560047\n",
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      "epoch 90 :finished\n",
      "train_loss: 0.38850944069844195\n"
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      "epoch 91 :finished\n",
      "train_loss: 0.3886112043031667\n"
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      "val_loss: 0.4249724883331389\n",
      "{'0': {'precision': 0.9068320096604895, 'recall': 0.9369001919385797, 'f1-score': 0.9216209199688465, 'support': 20840}, '1': {'precision': 0.4238921001926782, 'recall': 0.08874546187979024, 'f1-score': 0.14676450967311538, 'support': 2479}, '2': {'precision': 0.8328089436336858, 'recall': 0.8785663841807909, 'f1-score': 0.8550759502371297, 'support': 28320}, '3': {'precision': 0.8572465521413017, 'recall': 0.8089041095890411, 'f1-score': 0.8323740162105017, 'support': 8760}, '4': {'precision': 0.7454404775063557, 'recall': 0.7628959276018099, 'f1-score': 0.7540671996421983, 'support': 8840}, 'accuracy': 0.844264070827135, 'macro avg': {'precision': 0.7532440166269021, 'recall': 0.6952024150380024, 'f1-score': 0.7019805191463584, 'support': 69239}, 'weighted avg': {'precision': 0.832385368063118, 'recall': 0.844264070827135, 'f1-score': 0.8339766681954816, 'support': 69239}}\n",
      "epoch 92 :finished\n",
      "train_loss: 0.38856469717917774\n"
     ]
    },
    {
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     "text": [
      "val_loss: 0.4247139333067341\n",
      "{'0': {'precision': 0.9170731707317074, 'recall': 0.9291746641074856, 'f1-score': 0.9230842569419617, 'support': 20840}, '1': {'precision': 0.41638795986622074, 'recall': 0.10044372730939895, 'f1-score': 0.16184595385115375, 'support': 2479}, '2': {'precision': 0.839340464672617, 'recall': 0.8699858757062147, 'f1-score': 0.8543884592710753, 'support': 28320}, '3': {'precision': 0.8466792275082431, 'recall': 0.8207762557077626, 'f1-score': 0.8335265476466497, 'support': 8760}, '4': {'precision': 0.7269628099173554, 'recall': 0.7960407239819004, 'f1-score': 0.7599352051835854, 'support': 8840}, 'accuracy': 0.844581810829157, 'macro avg': {'precision': 0.7492887265392286, 'recall': 0.7032842493625524, 'f1-score': 0.7065560845788851, 'support': 69239}, 'weighted avg': {'precision': 0.8341745817057913, 'recall': 0.844581810829157, 'f1-score': 0.8355708917233337, 'support': 69239}}\n",
      "epoch 93 :finished\n",
      "train_loss: 0.38774739264603253\n"
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     "text": [
      "val_loss: 0.42189957386023896\n",
      "{'0': {'precision': 0.9131251467480629, 'recall': 0.9330614203454894, 'f1-score': 0.9229856413907678, 'support': 20840}, '1': {'precision': 0.41578947368421054, 'recall': 0.127470754336426, 'f1-score': 0.1951219512195122, 'support': 2479}, '2': {'precision': 0.8311619250199415, 'recall': 0.8830508474576271, 'f1-score': 0.8563210519106972, 'support': 28320}, '3': {'precision': 0.86500999000999, 'recall': 0.7907534246575343, 'f1-score': 0.8262166030534353, 'support': 8760}, '4': {'precision': 0.7473591549295775, 'recall': 0.7683257918552037, 'f1-score': 0.7576974564926373, 'support': 8840}, 'accuracy': 0.8447262381028033, 'macro avg': {'precision': 0.7544891380783565, 'recall': 0.700532447730456, 'f1-score': 0.7116685408134099, 'support': 69239}, 'weighted avg': {'precision': 0.8345429356594188, 'recall': 0.8447262381028033, 'f1-score': 0.8363125295268667, 'support': 69239}}\n",
      "epoch 94 :finished\n",
      "train_loss: 0.3875447835215029\n"
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      "val_loss: 0.4254828765437475\n",
      "{'0': {'precision': 0.9046232085067036, 'recall': 0.9389155470249521, 'f1-score': 0.9214504356016012, 'support': 20840}, '1': {'precision': 0.4129032258064516, 'recall': 0.10326744655102864, 'f1-score': 0.16521458535011294, 'support': 2479}, '2': {'precision': 0.8549547952490694, 'recall': 0.8514830508474577, 'f1-score': 0.8532153914197258, 'support': 28320}, '3': {'precision': 0.8342980365452276, 'recall': 0.8391552511415525, 'f1-score': 0.8367195947868649, 'support': 8760}, '4': {'precision': 0.7142284167251579, 'recall': 0.8057692307692308, 'f1-score': 0.7572423324297028, 'support': 8840}, 'accuracy': 0.8436141480957264, 'macro avg': {'precision': 0.744201536566522, 'recall': 0.7077181052668443, 'f1-score': 0.7067684679176015, 'support': 69239}, 'weighted avg': {'precision': 0.8334967946893244, 'recall': 0.8436141480957264, 'f1-score': 0.8347801064290132, 'support': 69239}}\n",
      "epoch 95 :finished\n",
      "train_loss: 0.3876079558114123\n"
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      "val_loss: 0.4228239251290364\n",
      "{'0': {'precision': 0.90976496922216, 'recall': 0.9361324376199616, 'f1-score': 0.9227603821776558, 'support': 20840}, '1': {'precision': 0.39690721649484534, 'recall': 0.09318273497377975, 'f1-score': 0.1509310682783404, 'support': 2479}, '2': {'precision': 0.8245146429746627, 'recall': 0.8847810734463277, 'f1-score': 0.8535854198603304, 'support': 28320}, '3': {'precision': 0.8385608856088561, 'recall': 0.8301369863013699, 'f1-score': 0.8343276732446077, 'support': 8760}, '4': {'precision': 0.7814992025518341, 'recall': 0.7205882352941176, 'f1-score': 0.7498087222647283, 'support': 8840}, 'accuracy': 0.8440185444619361, 'macro avg': {'precision': 0.7502493833704718, 'recall': 0.6929642935271113, 'f1-score': 0.7022826531651325, 'support': 69239}, 'weighted avg': {'precision': 0.8311491492629123, 'recall': 0.8440185444619361, 'f1-score': 0.8335626322698461, 'support': 69239}}\n",
      "epoch 96 :finished\n",
      "train_loss: 0.3871076807197609\n"
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    {
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     "output_type": "stream",
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     "text": [
      "val_loss: 0.42396631672317353\n",
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      "epoch 97 :finished\n",
      "train_loss: 0.38724440596006826\n"
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      "val_loss: 0.42343063590542007\n",
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      "epoch 98 :finished\n",
      "train_loss: 0.3867876570875718\n"
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    {
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     "output_type": "stream",
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      "val_loss: 0.42739440182606087\n",
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      "epoch 99 :finished\n",
      "train_loss: 0.3865008742348717\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\r",
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      "val_loss: 0.4276366023598118\n",
      "{'0': {'precision': 0.9019743200331355, 'recall': 0.9404510556621881, 'f1-score': 0.9208109187436867, 'support': 20840}, '1': {'precision': 0.40974212034383956, 'recall': 0.11536910044372731, 'f1-score': 0.18004406672961912, 'support': 2479}, '2': {'precision': 0.8389065003238232, 'recall': 0.8690324858757063, 'f1-score': 0.8537038000589695, 'support': 28320}, '3': {'precision': 0.8368916437098255, 'recall': 0.8323059360730594, 'f1-score': 0.8345924908424909, 'support': 8760}, '4': {'precision': 0.7563619764920689, 'recall': 0.7497737556561086, 'f1-score': 0.7530534567971369, 'support': 8840}, 'accuracy': 0.8436719190051849, 'macro avg': {'precision': 0.7487753121805386, 'recall': 0.7013864667421579, 'f1-score': 0.7084409466343806, 'support': 69239}, 'weighted avg': {'precision': 0.8317297809916601, 'recall': 0.8436719190051849, 'f1-score': 0.8345144092719144, 'support': 69239}}\n",
      "epoch 100 :finished\n",
      "train_loss: 0.3861272251747087\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "val_loss: 0.42287889030691433\n",
      "{'0': {'precision': 0.9115802284216439, 'recall': 0.9345009596928983, 'f1-score': 0.9228983034783433, 'support': 20840}, '1': {'precision': 0.4229452054794521, 'recall': 0.09963695038321904, 'f1-score': 0.16127979105452173, 'support': 2479}, '2': {'precision': 0.8465441150919136, 'recall': 0.860204802259887, 'f1-score': 0.8533197891307774, 'support': 28320}, '3': {'precision': 0.8416405251539445, 'recall': 0.8269406392694064, 'f1-score': 0.8342258305982609, 'support': 8760}, '4': {'precision': 0.7209044110225094, 'recall': 0.8079185520361991, 'f1-score': 0.7619352429722089, 'support': 8840}, 'accuracy': 0.8444518262828753, 'macro avg': {'precision': 0.7487228970338927, 'recall': 0.705840380728322, 'f1-score': 0.7067317914468225, 'support': 69239}, 'weighted avg': {'precision': 0.8342914897367257, 'recall': 0.8444518262828753, 'f1-score': 0.8354013705947739, 'support': 69239}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "EPOCH = 100\n",
    "loss_list=[]\n",
    "los_min=10**10\n",
    "val_loss_list=[]\n",
    "ac_list=[]\n",
    "\n",
    "for epoch in tqdm.tqdm(range(EPOCH)):\n",
    "    running_loss = 0.0\n",
    "    count=0\n",
    "    for _, (inputs, labels) in enumerate(train_dataloader, 0):\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        inputs1 = inputs.to(DEVICE)\n",
    "        labels = labels.to(DEVICE)\n",
    "        outputs = LSTMModel(inputs1)\n",
    "        \n",
    "        loss = criterion(outputs, labels.squeeze())\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        count=count+1\n",
    "\n",
    "        # print statistics\n",
    "        running_loss += loss.item()\n",
    "    loss_loss=running_loss/count\n",
    "    loss_list.append(loss_loss)\n",
    "    print('epoch',epoch+1,':finished')\n",
    "    print('train_loss:',loss_loss)\n",
    "    with torch.no_grad():\n",
    "        count=0\n",
    "        running_loss=0.0\n",
    "        pre=list()\n",
    "        lab=list()\n",
    "        for _, (inputs, labels) in enumerate(test_dataloader, 0):\n",
    "            inputs1 = inputs.to(DEVICE)\n",
    "            labels=labels.to(DEVICE)\n",
    "            outputs = LSTMModel(inputs1)\n",
    "            loss =criterion(outputs, labels.squeeze())\n",
    "            running_loss += loss.item()\n",
    "            count+=1\n",
    "            _, predicted = torch.max(F.softmax(outputs).data, 1)\n",
    "            predicted=predicted.to('cpu')\n",
    "            labels=labels.to('cpu')\n",
    "            predicted=predicted.tolist()\n",
    "            labels=labels.tolist()\n",
    "            pre.append(predicted)\n",
    "            lab.append(labels)\n",
    "        loss_loss=running_loss/count\n",
    "        val_loss_list.append(loss_loss)\n",
    "        pre=sum(pre,[])\n",
    "        lab=sum(lab,[])\n",
    "        print('val_loss:',loss_loss)\n",
    "        cl = classification_report(lab, pre,output_dict=True)\n",
    "        print(cl)\n",
    "        ac_list.append(cl['accuracy'])\n",
    "#         if los_min>loss_loss:\n",
    "#             los_min=loss_loss\n",
    "#             torch.save(Transmodel.state_dict(),'Trans_8lay_state') \n",
    "\n",
    "        torch.save(LSTMModel.state_dict(),'/project/hikaku_db/ziwei/Model_11/Model_11_state_3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(loss_list[1:])\n",
    "plt.plot(val_loss_list[1:])\n",
    "plt.savefig('/project/hikaku_db/ziwei/Model_11/loss_plt_state_3.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ac_list)\n",
    "plt.savefig('/project/hikaku_db/ziwei/Model_11/ac_plt_state_3.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_list = pd.DataFrame([loss_list])\n",
    "val_loss_list = pd.DataFrame([val_loss_list])\n",
    "ac_list = pd.DataFrame([ac_list])\n",
    "loss_list.to_csv('/project/hikaku_db/ziwei/Model_11/loss_list_state_3.csv',header=False,index=False)\n",
    "val_loss_list.to_csv('/project/hikaku_db/ziwei/Model_11/val_loss_list_state_3.csv',header=False,index=False)\n",
    "ac_list.to_csv('/project/hikaku_db/ziwei/Model_11/ac_list_state_3.csv',header=False,index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
